Optimization of Wire EDM Machining Parameters for Optimum Material Removal Rate and Surface Finish in an Aluminum 7075-T651 Alloy


Call for Papers Engineering Research Journal June 2019

Download Full-Text PDF Cite this Publication

Text Only Version

Optimization of Wire EDM Machining Parameters for Optimum Material Removal Rate and Surface Finish in an Aluminum 7075-T651 Alloy

Veeresh Murthy Research scholar Department of Mechanical Engineering

University Visvesvaraya College of Engineering Bangalore, India

Raju. G. S

PG Student Department of Mechanical

Engineering University Visvesvaraya College of Engineering

Bangalore, India

Saju. K. K PG Student

Department of Mechanical Engineering

University Visvesvaraya College of Engineering Bangalore, India

  1. M. Rajaprakash Professor

    Department of Mechanical Engineering

    University Visvesvaraya College of Engineering Bangalore, India

    Abstract – Wire EDM (Electrical Discharge Machining) is a thermos-electrical process in which material is eroded by a series of sparks between the work piece and the wire electrode (tool). In the present work, the machining of Aluminum Al 7075- T 651 during Wire cut Electrical Discharge Machining (Wire EDM) with Brass as a wire electrode has been carried out to study the operational behavior of Al 7075-T651 and to understand the effect of WEDM input parameters on Material removal rate (MRR) and surface roughness (Ra) of aluminum alloy and use of Taguchi technique to optimize the process parameters. The process parameters in Wire EDM are used to control the performance measures of the machining process. Process parameters are generally controllable machining input factors that determine the conditions in which machining is carried out. These machining conditions will affect the process performance result, which are gauged using various performance measures. In this research, Taguchi technique has been used to formulate the experimental layout and to obtain optimum levels of input parameters. ANOVA method is used to analyze the effect of each parameter on the machining characteristics namely (MRR and Ra) and predict the optimal choice for each Wire EDM parameters namely Pulse on time (TON), Servo Voltage (SV) and Pulse off time (TOFF).

    Keywords Wire cut Electrical Discharge Machining (WEDM), Taguchi technique, Analysis of Variation (ANOVA), Pulse on time (TON), Servo Voltage (SV), Pulse off time (TOFF).

    1. INTRODUCTION

      Machining is a term used to describe a variety of material removal processes in which a cutting tool removes unwanted material from a work piece to produce the desired shape. The term metal cutting is used when the material is metallic.

      centigrade, and the eroded work piece gets cooled down swifty in working liquid and is flushed away.

      Fig.1. Main Parts of Wire EDM

      The aim of this research paper is to determine the optimum WEDM process parameters using Taguchi technique for maximum MRR and minimum Surface roughness of Al 7075-T651 alloy.

    2. MATERIALS AND METHODOLOGY

      A. Materials Used

      Aluminum Al 7075-T651

      The alloy chosen for this project is Al 7075. It is one of the high strength alloys that render favorably heat treatment and its chemical composition is given in the Table 2.1 The selected aluminum alloy is shown in Figure 2.1.

      TABLE.1. Chemical Composition of Al7075 alloy

      Con tent

      Al

      Cu

      Mg

      Si

      Fe

      Mn

      Ni

      Pb

      Sn

      Ti

      Zn

      Cr

      Wei ght

      %

      90.245

      1.597

      2.215

      0.057

      0.257

      0.074

      0.047

      0.024

      0.010

      0.031

      5.206

      0.237

      Table 2.2 Process parameters and their ranges in Wire EDM

      Sl no

      Name of parameters

      Symbol

      Range

      1

      Pulse on time

      TON

      100-131 µs

      2

      Pulse off time

      TOFF

      00-63 µs

      3

      Peak current

      IP

      10-12 amps

      4

      Servo voltage

      SV

      00-99 volts

      5

      Wire feed

      WF

      01-15 m/min

      6

      Wire tension

      WT

      01-15 µN

      7

      Servo feed

      SF

      2000-2999

      mm/min

    3. EXPERIMENTATION PLAN

      1. Selection of Process parameters and their Levels

        In order to optimize the WEDM process for maximum Material Removal Rate and minimum Surface Roughness of aluminum alloy, the range of parameters are selected for control factors. The control factors selected are TON, TOFF and S.V. The range selected for TON is 100,103, 106 and for TOFF the range selected is 40,45, 50 and for S.V, the range selected is 15,20,25. Table 3.1 shows the control parameters selected and their corresponding levels with an objective to achieve maximum MRR and minimum surface roughness (Ra).

        Fig.2. Aluminum alloy Al 7075-T651

      2. Wire EDM Methodology

      Fig. 2.2 Wire EDM (Eco-Cut)

      Table 3.1 Control Parameters and their Levels

      Control factors

      Level 1

      Level 2

      Level 3

      (TON) Pulse on time (µs)

      100

      103

      106

      (TOFF) Pulse off time

      40

      45

      50

      (S.V) Servo Voltage (V)

      15

      20

      25

      1. Design of experiments and data analysis

        For the three control parameters and three levels selected, a set of 9 experiments are performed based on L9 orthogonal array as shown in Table 3.2. The MRR, Ra determined from experiments and Signal to Noise ratio computed are tabulated.

        In Wire EDM machine MRR is calculated by using the following equation,

        MRR= (Cutting speed)x(work height) (1)

        Where cutting speed in mm/min was measured during each trial and work height in mm is constant.

        Surface Roughness number (Ra) expressed in microns is determined by

        Ra = (p+p+—–+hn)/n (2)

        Where p, p p are the peak values and n is the number of peaks selected.

        The loss function (L) for objective of Higher is Better (HB) and Lower is Better (LB) are defined as follows:

        (3)

        LLB = (4)

        Where n indicates the number of experiments and y_MRR and yRa are the response for Material removal rate (MRR) and Surface Roughness (Ra) respectively .The S/N ratio can be calculated as a logarithmic transformation of the loss function as shown below.

        S/N ratio for MRR = -10 log10 (LHB) (5)

        S/N ratio for Ra = -10 log10 (LLB) (6)

        The S/N ratios and experimental measured values of MRR and Ra are computed using equations (1) to (6).

        Table 3.2 Experimental Design using L9 Orthogonal Array

        Trial no

        Ton (µs)

        Toff (µs)

        SV (V)

        MRR

        (mm2/min)

        Surface Roughness Ra (µm)

        S/N ratio MRR

        S/N ratio Ra

        1

        100

        40

        15

        47.52

        1.904

        33.5375

        -5.5933

        2

        100

        45

        20

        36.17

        1.960

        31.1670

        -5.8451

        3

        100

        50

        25

        26.24

        1.840

        28.3793

        -5.2964

        4

        103

        40

        20

        56.00

        3.177

        34.9638

        -10.0403

        5

        103

        45

        25

        44.00

        3.508

        32.8691

        -10.9012

        6

        103

        50

        15

        41.44

        3.126

        32.3484

        -9.8998

        7

        106

        40

        25

        88.32

        3.221

        38.9212

        -10.1598

        8

        106

        45

        15

        86.44

        3.418

        38.6332

        -10.6754

        9

        106

        50

        20

        56.70

        3.513

        35.0717

        -10.9136

      2. Analysis of Variation (ANOVA)

        In order to understand the impact of various factors and their interactions, analysis of variance (ANOVA) table is determined to find out the order of significant factors as well as interactions. Table 3.3 shows the results of the ANOVA with the material removal rate. The last column of the Table indicates that the main effects are highly significant (all having very small p- values). It can be concluded that TON (p=.029) and TOFF (p=0.091)) have greater influence on MRR. For surface roughness the ANOVA results are shown in Table 3.4, where TON (p=.017) have greater influence on surface roughness (Ra).

        Table 3.3 ANOVA table for MRR

        Source

        DOF

        SSA

        MSSA

        F-value

        P-value

        TON

        2

        2604.99

        1302.5

        33.71

        0.029

        TOFF

        2

        770.98

        385.49

        9.98

        0.091

        SV

        2

        110.73

        55.37

        1.43

        0.411

        Error

        2

        77.27

        38.63

        Total

        8

        3563.96

        Table 3.4 ANOVA table for Ra

        Source

        DOF

        SSA

        MSSA

        F-value

        P-value

        TON

        2

        4.0853

        2.04269

        57.59

        0.017

        TOFF

        2

        0.0597

        0.02989

        0.84

        0.543

        SV

        2

        0.0068

        0.00344

        0.70

        0.911

        Error (MSSE)

        2

        0.0709

        0.03547

        Total (SST)

        8

        4.2229

      3. Determination Of Material Removal Rate (Mrr) And Surface Roughness (Ra)

      Material removal rate is the amount of material removed per unit time by the cutting tool. Material removal rate (mm2/min) in a WEDM process is determined by multiplying the cutting speed(mm/min) and Work height (mm). Surface Roughness is the measure of the texture of the surface. It is quantified by the vertical deviations of a real surface from its ideal one. If these variations is maximum then surface is rough surface, if variations is minimum then the surface is smooth. The measurements are usually made along a line, running at right angle to the general direction of tool marks on the surface and expressed in micrometer.. The Figure 3.1 shows the Mitutoyo Surface Roughness tester used to measure the surface roughness.

      Fig. 3.1 Mitutoyo Surface Roughness tester

    4. RESULTS AND DISCUSSION

  1. Determination of optimum parameters

    The effect of three process parameters on MRR and Ra is shown graphically in Figure 4.1 and Figure 4.2. From the main effect plots, the level corresponding to the higher S/N ratio is obtained. Using MINITAB 18, response tables for S/N ratio of MRR and Ra is calculated as shown in Table 4.1 and Table 4.2. Based on the analysis of S/N ratio, the optimum process parameters for MRR and Ra along with their corresponding levels are obtained and is shown in Table

      1. and Table 4.4.

        Parameter

        Level

        Optimum Value B. Material Removal Rate (MRR)

        TON

        3

        106 Using optimum process parameters obtained for higher MRR

        TOFF

        1

        40 (TON =100, TOFF =40, SV = 15), machining was conducted to

        determine material removal rate and by using optimum

        SV

        1

        15 parameters obtained for Ra (TON =106, TOFF =40, SV = 15),

        Parameter

        Level

        Optimum Value B. Material Removal Rate (MRR)

        TON

        3

        106 Using optimum process parameters obtained for higher MRR

        TOFF

        1

        40 (TON =100, TOFF =40, SV = 15), machining was conducted to

        determine material removal rate and by using optimum

        SV

        1

        15 parameters obtained for Ra (TON =106, TOFF =40, SV = 15),

        Fig 4.1 Main effects plot for MRR

        Fig 4.2 Main effects plot for Ra

        Table 4.1 Response table for S/N ratio of MRR

        Levels

        A (TON)

        B (TOFF)

        C (SV)

        1

        31.03

        35.81

        34.84

        2

        33.39

        34.22

        33.73

        3

        37.54

        31.93

        33.39

        Delta

        6.51

        3.87

        1.45

        Rank

        1

        2

        3

        Table 4.2 Response table for S/N ratio of Ra

        Level

        A (TON)

        B (TOFF)

        C(SV)

        1

        -5.578

        -8.598

        -8.723

        2

        -10.280

        -9.141

        -8.933

        3

        -10.583

        -8.703

        -8.786

        Delta

        5.005

        0.543

        0.210

        Rank

        1

        2

        3

        From Figure 4.1 and Figure 4.2, the optimum process parameters for maximum MRR and minimum Ra is identified and shown in Table 4.3 and Table 4.4.

        Table 4.3 Optimum Process parameters for higher MRR

        Parameter

        Level

        Optimum

        TON

        1

        100

        TOFF

        1

        40

        SV

        1

        15

        Table 4.4 Optimum Process parameters for lower Ra

        surface roughness of Aluminum alloy was determined. Table

        4.5 and Table 4.6 lists the MRR and Ra of Aluminum alloy.

        Table 4.5 Material removal rate of Aluminum alloy

        Trial

        #

        Material Removal Rate (MRR), mm2/min

        Average MRR mm2/min

        01

        87.56

        88.24

        02

        86.15

        03

        83.20

        04

        96.05

        Table 4.6 Surface Roughness of Aluminum alloy

        Trial #

        Surface Roughness (Ra) in m

        Average Ra (m)

        01

        1.895

        1.904

        02

        1.913

        03

        1.908

        04

        1.900

        1. Confirmation experiment for Ultimate Tensile Strength

          Table 4.7 Prediction v/s Experimental result for MRR

          Optimal machining parameters

          Prediction

          Experimental

          Level

          TON 3, TOFF 1, SV 1

          TON 3, TOFF 1, SV 1

          S/N Ratio for MRR

          40.23

          38.91

          MRR

          89.12

          88.24

          percentage error

          0.99%

          Optimal machining parameters

          Prediction

          Experimental

          Level

          TON 1, TOFF 1, SV 1

          TON 1, TOFF 1, SV 1

          S/N Ratio for Ra

          -5.2490

          -5.5933

          Ra

          1.83

          1.904

          percentage error

          4.04 %

          1. S. S. Mahapatra & Amar Patnaik, Optimization of Wire Electrical Discharge Machining (WEDM) process parameters using Taguchi method, Journal of the Braz. Soc. of Mech. Sci. & Eng. 2006; 28: 422- 429.

          2. Pujari Srinivasa Rao, KoonaRamji, BeelaSatyanarayan, Effect of wire EDM conditions on generation of residual stresses in machining of aluminum 2014 T6alloy, Alexandria Engineering Journal, 2016; 55: 10771084.

          3. J.UdayaPrakash, T.V.Moorthy, J.MiltonPeter, Experimental

          Optimal machining parameters

          Prediction

          Experimental

          Level

          TON 1, TOFF 1, SV 1

          TON 1, TOFF 1, SV 1

          S/N Ratio for Ra

          -5.2490

          -5.5933

          Ra

          1.83

          1.904

          percentage error

          4.04 %

          1. S. S. Mahapatra & Amar Patnaik, Optimization of Wire Electrical Discharge Machining (WEDM) process parameters using Taguchi method, Journal of the Braz. Soc. of Mech. Sci. & Eng. 2006; 28: 422- 429.

          2. Pujari Srinivasa Rao, KoonaRamji, BeelaSatyanarayan, Effect of wire EDM conditions on generation of residual stresses in machining of aluminum 2014 T6alloy, Alexandria Engineering Journal, 2016; 55: 10771084.

          3. J.UdayaPrakash, T.V.Moorthy, J.MiltonPeter, Experimental

          TABLE 4.8 PREDICTION V/S EXPERIMENTAL RESULT FOR RA

          1. CONCLUSION

The experimental investigation on the optimization of WEDM process parameters of Aluminum alloy for MRR and Ra leads to the following conclusions:

      • The optimum process parameters in WEDM for higher MRR and lower Ra is determined for Al 7075-T651 alloy using Taguchis technique. WEDM parameters for higher MRR determined is TON=106, TOFF=40 and SV=15 and for lower Ra, TON=100, TOFF=40 and SV=15.

      • TON is the most significant WEDM parameter for higher MRR and lower Ra.

      • The experimental and predicted values of MRR and Ra shows good agreement.

      • The experimental MRR and Ra value of aluminum alloy for optimum WEDM parameters is 88.24 mm2/min and 1.904m respectively. The predicted MRR and Ra value of aluminum alloy for optimum WEDM parameters is 89.12 mm2/min and 1.83m respectively.

Investigations on Machinability of Aluminum Alloy (A413)/ Flyash/B4C Hybrid Composites Using Wire EDM, Procedia Engineering, 2013; l64, 1304-1354.

REFERENCES

  1. Lokeswara Rao T, N. Selvaraj, Optimization of Wire EDM Process Parameters on Titanium alloy using Taguchi Method, International Journal of Modern Engineering Research, 2013; 3 (4):2281-2286 .

  2. Pravin R. Kubade, Sunil S. Jamadade, Ravindranath G. Kshirsagar, Rahul C. Bhedasgaonkar, Parametric Study and Optimization of WEDM Parameters for Titanium diboride TiB2, International Research Journal of Engineering and Technology, 2015; 02 (04): 1657- 1661.

  3. Sivaraman B, Eswaramoorthy C, Shanmugham E.P, Optimal control parameters of machining in CNC Wire-Cut EDM for Titanium, Int. Journal of Applied Sciences and Engineering Research, 2015; 4 (1): 102-121.

  4. K.HHoS, T.Newman, S.Rahimifard,,R.DAllen State of the art in wire electrical discharge machining (WEDM), International Journal of machine tools and manufacture. 2004; 44 (12,13):1247-1259

  5. S V Subrahmanyam M., M. M. Sarcar, Evaluation of Optimal Parameters for machining with Wire cut EDM Using Grey-Taguchi Method, International Journal of Scientific and Research Publications, 2013; 3,(3) 768 ISSN 2250-3153.

  6. H Amini, M R Soleymani Yazdi, G H Dehghan, Optimization of process parameters in wire electrical discharge machining of TiB2 nanocomposite ceramic, 2011 Research Article Proceedings of the Institute of Mechanical Engineers, Part B, jounal of Engineering manufacture.

  7. Reza AzarAfza , RezaTeimouri, Experimental investigation, intelligent modeling and multi-characteristics optimization of dry WEDM process of AlSiC metal matrix composite , Journal of manufacturing processes, 2013;15.(4 ) 483-494.

Leave a Reply

Your email address will not be published. Required fields are marked *